AFCP Data Security Model for EHR Data Using Blockchain

D.; J. Jeno; R.; D.; G.

doi:https://doi.org/10.54216/JCIM.150103

International Journal of Neutrosophic Science | 2025

Neutrosophic Analytical Hierarchy Process (NAHP) for Addressing Cyber violence

To address the complex challenges of cyberviolence and gender-based violence among young students, it is crucial to employ analytical approaches that consider the multifaceted nature of these phenomena. The Neutrosophic Analytical Hierarchy (NAHP) method is presented as an innovative tool that allows us to unravel the different layers of influences and factors involved in these behaviors. This approach not only recognizes the diversity of perspectives and experiences that contribute to online and gender-based violence, but also offers a structured framework to assess and prioritize these factors holistically. By applying the NAHP, not only the visible and direct aspects of cyberviolence and gender violence are explored, but also the more subtle and underlying aspects that may go unnoticed in conventional analyses. This method allows us to capture the dynamic complexity of how individual perceptions, social norms, and power dynamics interact to perpetuate these problems in student environments. Thus, a deeper and more nuanced understanding of the triggering and contributing factors is fostered, facilitating the formulation of more effective interventions and policies that are sensitive to the specific needs of affected young people.

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Patricia Estefanía Rodríguez Palomo mail -

Sandra Giuliana Suárez Peña mail -

Paola Estefanía Salinas Aguilar mail -

Sanjar Mirzaliev mail

link https://doi.org/10.54216/IJNS.250139

Volume & Issue

Vol. Volume 25 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2025

Application of Multi-Criteria Methods and Neutrosophic Logic for the Analysis of Productive Factors

This article explores the innovative application of multi-criteria methods and neutrosophic logic in the analysis of productive factors, highlighting how these approaches can offer a more nuanced and comprehensive view of industrial and business dynamics  ̣ Multicriteria methods allow different aspects to be evaluated simultaneously, considering complex variables that affect productivity and efficiency in various sectors  ̣ On the other hand, neutrosophic logic introduces a theoretical framework that manages the uncertainty and imprecision inherent in many business decisions, offering tools to better interpret and manage the variabilities and ambiguities that influence productive results  ̣ This integrative approach not only seeks to improve accuracy in the evaluation of critical factors such as cost, quality and time, but also to promote more informed and strategic decision making in competitive and changing environments  ̣ By combining rigorous analysis with interpretive flexibility, the door is opened to new methodologies that can effectively adapt to the complexities of the globalized market and the dynamic demands of consumers  ̣ This article examines case studies and practical examples to illustrate how these methods can be successfully applied in the optimization of production processes and in the formulation of business strategies that seek not only to remain competitive, but also to anticipate and proactively respond to emerging challenges  ̣

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Franklin A. Molina-Borja mail -

Wendy Maribel-Molina mail -

Wilmer L. Toul Ayala mail -

Freddy X. Guamangate-Chiguano mail -

Sanjar Mirzaliev mail

link https://doi.org/10.54216/IJNS.250140

Volume & Issue

Vol. Volume 25 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2025

ANOVA and the 2-Tuple Neutrosophic linguistic method: A case study to analyze the interaction between elements

In this article, an innovative approach is presented that combines analysis of variance (ANOVA) with the Neutrosophic 2-Tuple linguistic method to explore and analyze the complex interactions between elements in various contexts. ANOVA, known for its ability to decompose variance and detect significant differences between groups, is here merged with the Neutrosophic method, which provides tools to handle the uncertainty and linguistic ambiguity present in many real data sets. This methodological synergy not only expands analytical possibilities, but also allows for a more nuanced and profound interpretation of the relationships between variables, overcoming the limitations of traditional approaches that assume absolute certainty in the data. Through detailed case studies and practical examples, it is demonstrated how this hybrid model can be effectively applied in fields as diverse as scientific research, business management, and public policy evaluation. The results obtained illustrate how the combination of ANOVA and 2-Tuple Neutrosophic not only improves the precision of statistical analysis, but also enriches the understanding of complex phenomena by considering and modeling uncertainty in a more realistic and adaptable way to different contexts and scenarios.

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Luis Alonso Chicaiza Sánchez mail -

Patricia Marcela Andrade Aulestia mail -

Dildora Abduturapova mail

link https://doi.org/10.54216/IJNS.250141

Volume & Issue

Vol. Volume 25 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2025

A proposed SWOT analysis method for integrating indeterminate Likert scale with the neutrosophic AHP

In the fast-paced world of business decision-making, where clarity and precision are vital, an integrated approach that combines the indeterminate Likert scale with the neutrosophic Analytical Hierarchy Process (AHP) offers a fresh and enriching perspective for SWOT analysis. This innovative methodology not only allows us to capture the ambiguity inherent in human evaluations, but also enhances analytical depth by incorporating neutrosophic thinking, which considers elements of truth, falsehood and indeterminacy. Instead of traditional methods that often oversimplify complexities, this integrated approach facilitates a more nuanced and holistic assessment of strengths, weaknesses, opportunities and threats, thus providing a more robust and reliable basis for formulating business strategies. Additionally, the adoption of the indeterminate Likert scale, fused with the neutrosophic AHP, introduces conceptual flexibility that is particularly useful in contexts of uncertainty and changing market dynamics. This approach not only allows decision makers to better capture the subjective and often contradictory perceptions of experts, but also facilitates the weighing of multiple criteria in a coherent and logical manner. Doing so ensures that the strategies developed are not only thoughtful and detailed, but also adaptable to the fluctuating realities of the modern business environment. In short, this integrated approach is presented as a powerful and versatile tool for strategic planning, capable of transforming complex challenges into tangible opportunities through a deep and balanced understanding of organizational reality.

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Edilberto Chacón Marcheco mail -

Yánez Pinto Washington Eduardo mail -

Nancy Margoth Cueva Salazar mail -

Blanca Mercedes Toro Molina mail -

Lucia Monserrath Silva Déley mail -

Burkhon Dekhkonov mail

link https://doi.org/10.54216/IJNS.250142

Volume & Issue

Vol. Volume 25 / Iss. Issue 1

Details open_in_new

Neutrosophic and Information Fusion | 2024

The Effect of Changing Convolutional Neural Nets Parameters on EEG Signals Recognition Ratio

Brain Computer Interface (BCI), especially systems for recognizing brain signals using EEG (Electroencephalography), is one of the important research topics that arouse the interest of many researchers currently. Convolutional Neural Nets (CNN) is one of the most important deep learning classifiers used in this recognition process, but the parameters of this classifier have not yet been precisely defined so that it gives the highest recognition rate and the lowest possible training and recognition time. This research proposes a system for recognizing EEG signals using the CNN network, while studying the effect of changing the parameters of this network on the recognition rate, training time, and recognition time of brain signals, as a result the proposed recognition system was achieved 76.38 % recognition rate, And the reduction of classifier training time (3 seconds) by using Common Spatial Pattern (CSP) in the preprocessing of IV2b dataset, and a recognition rate of 76.533% was reached by adding a layer to the proposed classifier.

groups

Khaled Moaz mail

link https://doi.org/10.54216/NIF.030205

Volume & Issue

Vol. Volume 3 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2025

Development of Neutrosophic Cognitive Maps (NCM) for the Evaluation and Ranking of the Main Causes of the Appearance of Fruit Fly Pests

The development of Neutrosophic Cognitive Maps (NCM) for the evaluation and ranking of the main causes of the appearance of fruit fly pests represents a significant advance in the field of agriculture and entomology  ̣This innovative approach allows for a holistic and integrated view of the complex and often interdependent factors that contribute to the proliferation of these destructive pests  ̣Using neutrosophic theory, which incorporates degrees of truth, falsehood, and indeterminacy, NCMs offer a powerful tool for identifying and prioritizing critical variables  ̣In this way, a more nuanced and precise understanding of the phenomenon is facilitated, enabling the design of more effective and sustainable management strategies  ̣The methodology applied in the construction of the NCM is characterized by its ability to manage the uncertainty and ambiguity inherent to ecological and agricultural systems  ̣Through the participation of experts and the analysis of empirical data, maps can be outlined that reflect the real complexity of the problem  ̣These maps not only highlight direct causes, such as weather conditions and poor agricultural practices, but also address underlying and systemic factors  ̣Thus, the use of NCM provides a robust conceptual framework for informed decision making, improving the efficiency of interventions and contributing significantly to crop protection and global food security.

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Emerson Javier Jácome-Mogro mail -

Pablo Morales mail -

Cristian Jiménez-Jácome mail -

Dilfuza Abidova mail

link https://doi.org/10.54216/IJNS.250143

Volume & Issue

Vol. Volume 25 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2024

Enhancing Stock Market Trend Prediction Using Explainable Artificial Intelligence and Multi-source Data

Determining the trend of the stock market is a complex task influenced by numerous factors like fundamental variables, company performance, investor behavior, sentiments expressed in social media, etc. Although machine learning models support predicting stock market trends using historical or social media data, reliance on a single data source poses a serious challenge. This study introduces a novel Explainable artificial intelligence (XAI) to address a binary classification problem wherein the objective is to predict the trend of the stock market, utilizing an integration of multiple data sources. The dataset includes trading data, news and Twitter sentiment, and technical indicators. Sentiment analysis and the Natural Language Toolkit are utilized to extract the qualitative information from social media data. Technical indicators, or quantitative characteristics, are therefore generated from trade data. The technical indicators are fused with the stock sentiment features to predict the future stock market trend. Finally, a machine learning model is employed for upward or downward stock trend predictions. The proposed model in this study incorporates XAI to interpret the results. The presented model is evaluated using five bank stocks, and the results are promising, outperforming other models by reporting a mean accuracy of 90.14%. Additionally, the proposed model is explainable, exposing the rationale behind the classifier and furnishing a complete set of interpretations for the attained outcomes.

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John Ranjith mail -

Kumar Chandar S mail

link https://doi.org/10.54216/FPA.160211

Volume & Issue

Vol. Volume 16 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

Enhancing Cybersecurity: Detecting Hidden Information in Spatial Domain Images Using Convolutional Neural Networks

Steganography involves concealing hidden messages inside various types of media, whereas steganalysis is the process of identifying the presence of steganography. Convolutional neural networks (CNN), a type of neural network that outperformed previously proposed machine learning-based methods when introduced, are among the models used for deep learning. While CNN-based methods may yield satisfactory results, they face challenges in terms of classification accuracy and network training stability. The present research introduces a CNN structure to increase hidden data detection and spatial domain image training reliability. The suggested method includes pre-processing, feature extraction, and classification. Evaluation of performance is conducted on datasets Break Our Steganographic System Base (BOSSbase-.01) and Break Our Watermarking System (BOWS2) with three adaptive steganography algorithms. Wavelet Obtained Weights (WOW), Spatial Universal Wavelet Relative Distortion (S-UNIWARD), and Highly Undetectable steGO (HUGO) operating at low payload capacities of 0.2 and 0.4 bits per pixel (bpp). The experimental results surpass the accuracy and network stability of prior publications. Training accuracy ranges from 91% to 94%, and testing accuracy ranges from 74.8% to 86.65%.

groups

Akram Mshet mail -

Huda Tayyeh mail

link https://doi.org/10.54216/JCIM.150101

Volume & Issue

Vol. Volume 15 / Iss. Issue 1

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

Optimized and Comprehensive Fake Review Detection based on Harris Hawks optimization integrated with Machine Learning Techniques

Fake review detection, often known as spam review detection, is a crucial aspect of natural language processing. It involves extracting valuable information from text documents obtained from various sources. Various methodologies, such as simple rule-based approaches, lexicon-based methods, and advanced machine learning algorithms, have been extensively employed with diverse classifiers to provide accurate detection of fake reviews. Nevertheless, review classification based on lexicons continues to face challenges in achieving high accuracies, mostly because of the need for domain-specific comprehensive dictionaries. Furthermore, machine learning-driven review detection also addresses the limitations in accuracy caused by the uncertainty of features in social data. In order To address the problem of accuracy, one effective approach is to carefully choose the most optimal set of features and minimize the number of features used. The Objective of the research paper is to select a small subset of features out of the thousands of features for accurate classification of spam review detection. Therefore, a good feature selection method is needed in order to speed up the processing rate and predictive accuracy. This paper, Harris Hawks Optimization (HHO), is utilized for feature selection in sentiment analysis tasks. The performance of the selected feature subsets was evaluated using SVM, X-GBoost, ETC classifiers. Experimental results on tweet reviews for the airline dataset demonstrated superior sentiment classification capabilities, achieving an accuracy of 0.9435% with SVM and 0.9607%, 0.9635% for X-Boost, ETC, respectively.

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Zahraa Fadhel mail -

Hussien Attia mail -

Yossra Hussain Ali mail

link https://doi.org/10.54216/JCIM.150102

Volume & Issue

Vol. Volume 15 / Iss. Issue 1

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

AFCP Data Security Model for EHR Data Using Blockchain

The problem of data security in EHR is deeply concerning, as well as the methods used in session, feature, service, rule, and access restriction models. However, they fail to achieve higher security performance, which degrades the trust of data owners. To handle this issue, an efficient Adaptive Feature Centric Polynomial (AFCP) data security model is described here. The proposed method can be adapted to enforce security on any kind of data. The AFCP scheme classifies the features of EHR data under different categories based on their importance in being identified from the data taxonomy. By maintaining different categories of data encryption schemes and keys, the model selects a specific key for a unique feature with the use of the polynomial function. The method is designed to choose a dynamic polynomial function in the form of m(x) n, where the values of m and n are selected in a dynamic way. The method generates a blockchain according to the feature values and adapts the cipher text generated by applying a polynomial function to data encryption. The same has been reversed to produce the original EHR data by reversing the operation. The method enforces the Healthy Trust Access Restriction scheme in restricting malicious access. By adapting the AFCP model, the security performance is improved by up to 98%, and access restriction performance is improved by up to 97%. The proposed method increases the access restriction performance in the ratio of 19%, 16%, and 11% to HCA-ECC, EHRCHAIN, and PCH methods. Similarly, security performance is increased by 17% 13%, and 11% to HCA-ECC, EHRCHAIN, and PCH methods.

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D. Selvaraj mail -

J. Jeno Jasmine mail -

R. Ramani mail -

D. Dhinakaran mail -

G. Prabaharan mail

link https://doi.org/10.54216/JCIM.150103

Volume & Issue

Vol. Volume 15 / Iss. Issue 1

Details open_in_new